A neural model for the p-median problem

There exist several neural techniques for solving NP-hard combinatorial optimization problems. At the beginning of the 1908s, recurrent neural networks were shown to be able to solve optimization problems. Criticism of this approach includes the tendency of recurrent neural networks to produce infeasible solutions and poor local minima. This paper proposes a new technique which always provides feasible solutions and removes the tuning phase since the constraints are incorporated in the neural architecture instead of the energy function, therefore the tuning parameters are unnecessary.